Psychiatry

Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges.

Clinical neuroscience is increasingly relying on the collection of large volumes of differently structured data and the use of intelligent algorithms for data analytics. In parallel, the ubiquitous collection of unconventional data sources (e.g. mobile health, digital phenotyping, consumer neurotechnology) is increasing the variety of data points. Big data analytics and approaches to Artificial In...

Hierarchical Structured Sparse Learning for Schizophrenia Identification.

Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based schizophrenia (SZ) diagnosis. However, previous studies usually measure the fALFF within low-frequency fluctuation (from 0.01 to 0.08Hz), which cannot fully cover the complex neural activity pattern in the resting-state brain. In addition, existing ...

Quantifying the Association Between Psychotherapy Content and Clinical Outcomes Using Deep Learning.

IMPORTANCE: Compared with the treatment of physical conditions, the quality of care of mental health disorders remains poor and the rate of improvemen...

Prediction of Sex-Specific Suicide Risk Using Machine Learning and Single-Payer Health Care Registry Data From Denmark.

IMPORTANCE: Suicide is a public health problem, with multiple causes that are poorly understood. The increased focus on combining health care data wit...

Convolutional Neural Network Visualization for Identification of Risk Genes in Bipolar Disorder.

BACKGROUND: Bipolar disorder (BD) is a type of chronic emotional disorder with a complex genetic structure. However, its genetic molecular mechanism i...

Addressing the Credit Assignment Problem in Treatment Outcome Prediction using Temporal Difference Learning.

Mental health patients often undergo a variety of treatments before finding an effective one. Improved prediction of treatment response can shorten th...

Targeted prescription of cognitive-behavioral therapy versus person-centered counseling for depression using a machine learning approach.

OBJECTIVE: Depression is a highly common mental disorder and a major cause of disability worldwide. Several psychological interventions are available,...

Personalized prognostic prediction of treatment outcome for depressed patients in a naturalistic psychiatric hospital setting: A comparison of machine learning approaches.

OBJECTIVE: Research on predictors of treatment outcome in depression has largely derived from randomized clinical trials involving strict standardizat...

Preliminary Test of the Potential of Contact With Dogs to Elicit Spontaneous Imitation in Children and Adults With Severe Autism Spectrum Disorder.

IMPORTANCE: Finding strategies to enhance imitation skills in people with autism spectrum disorder (ASD) is of major clinical relevance.

[Autism spectrum disorder biomarkers based on biosignals, virtual reality and artificial intelligence].

It has been observed that the stratification of Autism Spectrum Disorders (ASD) generated by the current scales is not effective for the personalizati...

[Theoretical Review of Robot Anxiety].

The aim of the study is to describe the effect of robot anxiety. The forming of the concept and the description of similar constructs, like technologi...

Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder.

Repetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder (MDD) is associated with changes in brain functional connec...

Machine learning approach to identify a resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder.

Endophenotype refers to a measurable and heritable component between genetics and diagnosis, and the same endophenotype is present in both individuals...

Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks.

Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of A...

What health records data are required for accurate prediction of suicidal behavior?

OBJECTIVE: The study sought to evaluate how availability of different types of health records data affect the accuracy of machine learning models pred...

A computational study of mental health awareness campaigns on social media.

As public discourse continues to progress online, it is important for mental health advocates, public health officials, and other curious parties and ...

Predicting post-experiment fatigue among healthy young adults: Random forest regression analysis.

The current study utilized a random forest regression analysis to predict post-experiment fatigue in a sample of 212 healthy participants (mean age = ...

Applying Natural Language Processing to Evaluate News Media Coverage of Bullying and Cyberbullying.

Bullying events have frequently been the focus of coverage by news media, including news stories about teens whose death from suicide was attributed t...

CARERAID: Controlled Autonomous Robot for Early Detection and Rehabilitation of Autism and Intellectual Disability.

The Language of LGBTQ+ Minority Stress Experiences on Social Media.

LGBTQ+ (lesbian, gay, bisexual, transgender, queer) individuals are at significantly higher risk for mental health challenges than the general populat...